Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

My company, Kickfurther, has carved out a niche by connecting businesses in need of funding for their retail inventory with buyers of that inventory. A key component of this business model is the ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Among patients with chronic noncancer pain, a novel machine learning model effectively predicts opioid use disorder risk.

Machine learning model improves transplant risk assessment for patients with myelofibrosis, helping clinicians make informed decisions, as per an expert. A new machine learning model has significantly ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Researchers used advanced machine learning to increase the accuracy of a national cardiovascular risk calculator while preserving its interpretability and original risk associations. Risk calculators ...

Sensor data from wearable devices analyzed over five years reveals walking and posture differences that predict fall risk in Parkinson’s patients. Study: Predicting future fallers in Parkinson’s ...

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...